To simulate Unmanned Aerial System (UAS) based Vehicular Ad-hoc Networks (VANET) projects using MATLAB has includes to design the communication among UAVs (Unmanned Aerial Vehicles), ground vehicles, and the communication network which enables both systems to cooperate. UAS can perform as aerial relays, delivers better coverage and interaction among vehicles in a VANET.
The main aim is UAS-based VANET projects has contain mobility modelling, communication range evaluation, network topology, routing protocols, and QoS (Quality of Service) valuation. The UAVs can support in overwhelming the disadvantage of traditional VANET, like connectivity concerns in urban scenarios or highly dynamic mobility environment.
Here’s a step-by-step guide to simulate UAS-based VANET projects using MATLAB:
Steps to Simulate UAS based VANET Projects in MATLAB
Step 1: Install Required Toolboxes
Make sure that we have the following MATLAB toolboxes installed:
- Communications Toolbox (for mimic network communication protocols)
- Antenna Toolbox (optional, for replicating signal propagation and coverage)
- Optimization Toolbox (for enhancing routing and resource allocation)
- Simulink (for complex, large-scale system replication)
- Robotics System Toolbox (optional, for UAV path planning and control)
Step 2: Define UAS and VANET Network Parameters
Describe key metrics for UAVs, vehicles, and the network, that contain mobility patterns, communication ranges, and network protocols.
Example: Define System Parameters for UAS-based VANET
% Define UAS (UAV) and VANET parameters
numUAVs = 3; % Number of UAVs
numVehicles = 10; % Number of ground vehicles
commRangeUAV = 1000; % Communication range of UAVs (in meters)
commRangeVehicle = 300; % Communication range of ground vehicles (in meters)
uavSpeed = 15; % UAV speed (in m/s)
vehicleSpeed = 10; % Ground vehicle speed (in m/s)
networkBandwidth = 10e6; % Network bandwidth (10 Mbps)
dataPacketSize = 1e6; % Data packet size (1 MB)
Step 3: Define Mobility Model for UAVs and Vehicles
Mobility modeling is vital in UAS-based VANET simulations while both UAVs and ground vehicles are highly mobile. The UAVs can follow scheduled flight paths, since vehicles usually follow random or road-based mobility models.
Example: Define Mobility Models
% Random mobility for UAVs (fly within a predefined area)
uavPositions = rand(numUAVs, 2) * 2000; % Random UAV positions in a 2000×2000 m area
% Random mobility for vehicles (drive within a city grid)
vehiclePositions = rand(numVehicles, 2) * 1000; % Random vehicle positions in a 1000×1000 m area
% UAV and vehicle mobility simulation
uavVelocity = uavSpeed * [cos(rand(numUAVs, 1)*2*pi), sin(rand(numUAVs, 1)*2*pi)]; % Random UAV direction
vehicleVelocity = vehicleSpeed * [cos(rand(numVehicles, 1)*2*pi), sin(rand(numVehicles, 1)*2*pi)]; % Random vehicle direction
% Update UAV and vehicle positions over time
timeSteps = 100;
for t = 1:timeSteps
uavPositions = uavPositions + uavVelocity; % Update UAV positions
vehiclePositions = vehiclePositions + vehicleVelocity; % Update vehicle positions
% Ensure UAVs and vehicles stay within the boundaries (wrap-around)
uavPositions = mod(uavPositions, 2000);
vehiclePositions = mod(vehiclePositions, 1000);
end
Step 4: Communication Modeling Between UAVs and Vehicles
Design the communication among UAVs and vehicles. Each node (UAV or vehicle) can interact with others in its communication range. We can utilize basic models such as Euclidean distance to validate the connectivity among nodes.
Example: Model Communication Between UAVs and Vehicles
% Calculate distance between UAVs and vehicles
distances = pdist2(uavPositions, vehiclePositions);
% Determine connectivity based on communication range
uavToVehicleConnections = distances < commRangeUAV; % 1 if within range, 0 otherwise
% Display the communication connections between UAVs and vehicles
disp(‘UAV to Vehicle Connections:’);
disp(uavToVehicleConnections);
Step 5: Routing Protocols Simulation
VANETs usually utilize routing protocols like AODV (Ad-hoc On-demand Distance Vector) or DSR (Dynamic Source Routing). We can mimic on how routing tables are sustained and updated among UAVs and vehicles.
Example: Implement Simple Routing Protocol (Flooding)
% Simulate a simple flooding-based routing protocol
% Assume each vehicle or UAV sends data to all nodes within its range
numMessages = 5; % Number of data messages to send
for msg = 1:numMessages
% Randomly select a vehicle or UAV as the source
sourceNode = randi([1, numUAVs + numVehicles]);
if sourceNode <= numUAVs
% Source is a UAV
disp([‘UAV ‘, num2str(sourceNode), ‘ is sending a message.’]);
else
% Source is a vehicle
disp([‘Vehicle ‘, num2str(sourceNode – numUAVs), ‘ is sending a message.’]);
end
% Determine which nodes receive the message (within communication range)
if sourceNode <= numUAVs
receivers = uavToVehicleConnections(sourceNode, :); % UAV to vehicle communication
else
receivers = distances(:, sourceNode – numUAVs) < commRangeVehicle; % Vehicle to vehicle/UAV communication
end
% Display the message transmission results
disp([‘Nodes receiving the message: ‘, num2str(find(receivers))]);
end
Step 6: Simulate Quality of Service (QoS)
QoS parameters like latency, throughput, and packet loss are significant for UAS-based VANETs. We can mimic and estimate these parameters to evaluate the performance of network.
Example: Calculate Network Latency and Throughput
% Replicate latency according to distance among nodes and network bandwidth
latencyMatrix = distances / (networkBandwidth / dataPacketSize); % Simple latency model
% Simulate throughput based on network bandwidth and data size
throughput = networkBandwidth * timeSteps / numMessages; % Throughput in bits per second
disp([‘Average network latency: ‘, num2str(mean(latencyMatrix(:))), ‘ seconds’]);
disp([‘Average network throughput: ‘, num2str(throughput / 1e6), ‘ Mbps’]);
Step 7: Model UAV Relays and Traffic Forwarding
UAVs can perform as relays to expand communication coverage among vehicles which are distant away from each other or congested by difficulties.
Example: Simulate UAV Relay Behavior
% Simulate UAV relaying data between two distant vehicles
sourceVehicle = 1; % Vehicle 1 is the source
destinationVehicle = 5; % Vehicle 5 is the destination
% Check if direct communication is possible
if distances(sourceVehicle, destinationVehicle) > commRangeVehicle
% Use a UAV as a relay
for uav = 1:numUAVs
if distances(sourceVehicle, uav) < commRangeUAV && distances(uav, destinationVehicle) < commRangeUAV
disp([‘UAV ‘, num2str(uav), ‘ is relaying data between Vehicle ‘, num2str(sourceVehicle), ‘ and Vehicle ‘, num2str(destinationVehicle)]);
break;
end
end
else
disp([‘Direct communication between Vehicle ‘, num2str(sourceVehicle), ‘ and Vehicle ‘, num2str(destinationVehicle), ‘ is possible’]);
end
Step 8: Full System Simulation Using Simulink (Optional)
For a more comprehensive and complex system, we can utilize Simulink to design UAS-based VANET systems. We can make block illustrations for the vehicles, UAVs, communication channels, and data flow. The Simulink’s built-in support for mobility models and signal propagation enable for more perfect simulations.
Step 9: Visualize Network Topology and Communication
We can envision the movement of UAVs, vehicles, and the connections among them using MATLAB’s plotting functions.
Example: Plot the Network Topology
% Plot UAV and vehicle positions and their communication ranges
figure;
plot(uavPositions(:, 1), uavPositions(:, 2), ‘ro’, ‘MarkerSize’, 10, ‘DisplayName’, ‘UAVs’); hold on;
plot(vehiclePositions(:, 1), vehiclePositions(:, 2), ‘bx’, ‘MarkerSize’, 8, ‘DisplayName’, ‘Vehicles’);
legend(‘UAVs’, ‘Vehicles’);
% Plot communication range circles for UAVs and vehicles
for i = 1:numUAVs
viscircles(uavPositions(i, :), commRangeUAV, ‘LineStyle’, ‘–‘, ‘EdgeColor’, ‘r’);
end
for i = 1:numVehicles
viscircles(vehiclePositions(i, :), commRangeVehicle, ‘LineStyle’, ‘–‘, ‘EdgeColor’, ‘b’);
end
title(‘UAS-based VANET Network Topology’);
xlabel(‘X Position (m)’);
ylabel(‘Y Position (m)’);
grid on;
Step 10: Analyze Network Performance Metrics
Utilize MATLAB to estimate and measure the significant parameters like connectivity, network coverage, latency, and throughput.
Example: Calculate Connectivity
% Calculate the percentage of vehicles connected to at least one UAV or other vehicle
connectedVehicles = sum(any(uavToVehicleConnections, 1)) / numVehicles * 100;
disp([‘Percentage of vehicles connected to the network: ‘, num2str(connectedVehicles), ‘%’]);
In this setup simulation, we clearly understood the concepts regarding the Unmanned Aerial System (UAS) based Vehicular Ad-hoc Networks project that were simulated, visualized using the tool of MATLAB that has to evaluate the performance for Unmanned Aerial System (UAS) based Vehicular Ad-hoc Networks. Additional specific details regarding this project will be provided in another manual, if needed. If you’re searching for the top simulation and novel topics for your UAS-based VANET research, we can offer you personalized assistance.
